Advances in computing hardware and software, as well as computing networks and network services, have bolstered growth of Internet-based product and service procurement and delivery. For example, online shopping, in turn, has fostered the use of “subscription”-based delivery computing services with an aim to provide convenience to consumers. In particular, a user becomes a subscriber when associated with a subscriber account, which is typically implemented on a remote server for a particular seller. In exchange for electronic payment, which is typically performed automatically, a seller ships a specific product (or provides access to a certain service) at periodic times, such as every three (3) months, every two (2) weeks, etc., or any other repeated periodic time intervals. With conventional online subscription-based ordering, consumers need not plan to reorder to replenish supplies of a product.
But conventional approaches to provide subscription-based order fulfillment, while functional, suffer a number of other drawbacks. For example, traditional subscription-based ordering relies on computing architectures that predominantly generate digital “shopping cart” interfaces with which to order and reorder products and services. Traditional subscription-based ordering via shopping cart interfaces generally rely on a user to manually determine a quantity and a time period between replenishing shipments, after which the quantity is shipped after each time period elapses. Essentially, subscribers receive products and services on “auto-pilot.”
So while the conventional approaches to implementing shopping cart interfaces may be functional for stable rates of consumption, such approaches are not well-suited to facilitate timely reordering of products and services with which consumers may use at rates that vary from the fixed periods of time between repeated deliveries. Thus, conventional approaches to reordering or procuring subsequent product and services deliveries are plagued by various degrees of rigidity that interject sufficient friction into reordering that cause some users to either delay or skip making such purchases. Unfortunately, such friction causes some users to supplement the periodic deliveries manually if an item is discovered to be running low more quickly than otherwise might be the case (e.g., depleting coffee, toothpaste, detergent, wine, or any other product more quickly than normal).
Examples of such friction include “mental friction” that may induce stress and frustration in such processes. Typically, a user may be required to rely on one's own memory to supplement depletion of a product and services prior to a next delivery (e.g., remembering to buy coffee before running out) or time of next service. Examples of such friction include “physical friction,” such as weighing expending time and effort to either physically confront a gauntlet of lengthy check-out and shopping cart processes, or to make an unscheduled stop at a physical store.
Typical online approaches, including conventional shopping cart interfaces, suffer from less than optimal means with which to reconcile the different rates of product and service usage of different users. One prevalent consequence of mismatches between time periods for delivering subscribed products or services and consumption rates by consumers is that, over time, the supply of a subscribed item is either over-delivered or under-delivered. An oversupply of subscribed product or service typically degrades consumer experience due to a number of reasons. For example, subscribers may believe that a seller is “over-billing” the customer for unneeded products or services. Similarly, an under-supply of subscribed product may give to frustration and friction that an expected subscribed product or service is scarce or unavailable.
Online retailers and merchants may experience similar consequences due to mismatching of delivery times and consumption rates, such as at an aggregate level of subscribers. In the aggregate, the mismatches may cause either overstocking or understocking of inventory of the online retailers and merchants. Fluctuations in inventory may cause non-beneficial consumption of resources and time. Note, too, that the computing systems of online retailers and merchants are not well-adapted to address the above-described mismatching phenomena when ordering, shipping, and performing inventory management. These types of subscription models, therefore, are not generally well-suited for application to usual consumption rates of depletable products and services (e.g., product usage that depletes some or all of the product or service).
In some approaches, online retailers and merchants attempt to increase exposure and awareness of certain products by, for instance, providing samples of products to potential consumers. In one approach, a printed paper, such as a “flyer,” is typically added a box containing a purchased product being readied for shipment (e.g., from a warehouse). The flyer usually conveys information about a product in which a consumer may purchase in the future. For example, a flyer may include a website or other contact information that accompanies a variety of snacks and directs a recipient how to order a favored snack from the variety of snacks. But prior to sampling a product, the “friction” encountered by a consumer to acquire the sample described in a flyer is relatively high (e.g., a low likelihood of future acquisition of the item). Consumers have little time to follow directions to receive a sample for a product they are not aware that they would like to receive. In a modified approach, a sample of the product (e.g., either free or at a nominal price) may be included in a boxed shipment and the flyer, which explains how to purchase a product should the consumer desire.
Conventional computing platforms that implement traditional online merchant techniques, while functional, suffer drawbacks that limit opportunities to receive information regarding one or more consumers' experiences regarding a product. Known software and applications, therefore, are suboptimal in determining information of consumer experiences. As manufacturers and retailers expend millions to billions of dollars in investing in manufacturing samples for distribution, the information received back via conventional software and applications have yet to reach their potential.
Thus, what is needed is a solution to facilitate techniques for dynamically process electronic data to generate location resultants to optimize physical product distribution in networked application and platform architectures, without the limitations of conventional techniques.